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Google BigQuery vs Snowflake: What are the differences?
## Key Differences between Google BigQuery and Snowflake
Google BigQuery and Snowflake are two popular cloud-based data warehouse platforms known for their scalability and performance. While both offer powerful analytical capabilities, there are key differences that users should consider when choosing between the two solutions.
1. **Architecture and Scalability**: Google BigQuery is fully managed by Google Cloud Platform and uses a shared architecture, allowing for automatic scalability and no need for manual tuning. On the other hand, Snowflake follows a multi-cluster, shared data architecture that provides more control over resource allocation and isolation for workloads.
2. **Concurrency and Performance**: Snowflake offers better support for concurrent workloads with its multi-cluster architecture, allowing for higher performance for complex queries and large datasets. In comparison, Google BigQuery may face limitations in handling high concurrency queries due to its shared architecture.
3. **Pricing Model**: Google BigQuery charges users based on the amount of data processed, which can be a cost-effective solution for organizations with sporadic query needs. Snowflake, on the other hand, follows a more traditional pricing model based on compute resources used, which can be advantageous for users with predictable workloads.
4. **Data Storage and Compression**: Google BigQuery uses columnar storage and automatic data compression techniques to reduce storage costs and improve query performance. In contrast, Snowflake utilizes a hybrid columnar data warehousing approach that combines the benefits of both row and columnar storage for optimized storage and query execution.
5. **Ecosystem and Integration**: Both Google BigQuery and Snowflake offer robust ecosystems and integrations with popular BI tools, data sources, and languages. However, Snowflake provides more native connectors and support for third-party tools, making it easier to integrate with existing workflows and applications.
6. **Security and Compliance**: Snowflake offers advanced security features such as end-to-end encryption, granular access controls, and data governance capabilities. While Google BigQuery also provides strong security measures, Snowflake is often preferred by organizations with strict compliance requirements due to its focus on data protection and governance.
In Summary, Google BigQuery and Snowflake differ in their architecture, scalability, pricing model, performance, ecosystem, and security features, making it essential for organizations to evaluate their specific needs and priorities when choosing a cloud data warehouse solution.
Cloud Data-warehouse is the centerpiece of modern Data platform. The choice of the most suitable solution is therefore fundamental.
Our benchmark was conducted over BigQuery and Snowflake. These solutions seem to match our goals but they have very different approaches.
BigQuery is notably the only 100% serverless cloud data-warehouse, which requires absolutely NO maintenance: no re-clustering, no compression, no index optimization, no storage management, no performance management. Snowflake requires to set up (paid) reclustering processes, to manage the performance allocated to each profile, etc. We can also mention Redshift, which we have eliminated because this technology requires even more ops operation.
BigQuery can therefore be set up with almost zero cost of human resources. Its on-demand pricing is particularly adapted to small workloads. 0 cost when the solution is not used, only pay for the query you're running. But quickly the use of slots (with monthly or per-minute commitment) will drastically reduce the cost of use. We've reduced by 10 the cost of our nightly batches by using flex slots.
Finally, a major advantage of BigQuery is its almost perfect integration with Google Cloud Platform services: Cloud functions, Dataflow, Data Studio, etc.
BigQuery is still evolving very quickly. The next milestone, BigQuery Omni, will allow to run queries over data stored in an external Cloud platform (Amazon S3 for example). It will be a major breakthrough in the history of cloud data-warehouses. Omni will compensate a weakness of BigQuery: transferring data in near real time from S3 to BQ is not easy today. It was even simpler to implement via Snowflake's Snowpipe solution.
We also plan to use the Machine Learning features built into BigQuery to accelerate our deployment of Data-Science-based projects. An opportunity only offered by the BigQuery solution
Pros of Google BigQuery
- High Performance28
- Easy to use25
- Fully managed service22
- Cheap Pricing19
- Process hundreds of GB in seconds16
- Big Data12
- Full table scans in seconds, no indexes needed11
- Always on, no per-hour costs8
- Good combination with fluentd6
- Machine learning4
- Easy to manage1
- Easy to learn0
Pros of Snowflake
- Public and Private Data Sharing7
- Multicloud4
- Good Performance4
- User Friendly4
- Great Documentation3
- Serverless2
- Economical1
- Usage based billing1
- Innovative1
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Cons of Google BigQuery
- You can't unit test changes in BQ data1
- Sdas0